6 Ways AI is Changing the Financial Planning Industry

Artificial Intelligence (AI) is making financial planners more efficient and connected with their clients. On a larger level, integrating AI across the Financial Planning and Analysis (FP&A) industry is maximising opportunities and mitigating risks – allowing organisations to further benefit from this technology.

Currently, AI and Machine Learning are some of the fastest-moving fintech trends. For many industries within the finance sector, harnessing these technologies is crucial to stay afloat in a hotly competitive market.

On that note, let’s look at the different ways AI is changing and improving financial planning & analysis and how the industry can benefit from AI financial planning.

How AI Benefits Financial Planning & Analysis

FP&A teams deal with a lot of data. FP&A analysts consider economic and business trends, review past company performance and forecast future trends in order to inform major business decisions.

As businesses strive to make data-driven decision making, AI can be a valuable part of this process. It can help develop better real-time and accurate reporting, it can speed up tedious tasks, it allows planners to make more accurate predictions, it can help drive collaboration and innovation, and it simplifies complex data analysis and modelling.

To sum up – AI makes FP&A faster, easier and smarter.

Top AI Trends In Financial Planning

AI can help the FP&A industry to thrive. For anyone who is within the FP&A industry or is studying to become a financial planner, AI is going to be a major focus in the coming months.

Already, AI is disrupting the industry and inducing positive results. For one, it can automate manual tasks like data entry, and, against increasingly challenging market conditions, AI has provided the potential to help wealth managers sustain and drive new growth. Most importantly, it has created the opportunity to create hyper-personalized customer experiences.

Here are some of the top ways AI is disrupting – and enhancing – the FP&A:

1. Trend analysis

Trend analysis is the first AI capability that has become part of the predictive analysis toolkit. It uses both human interaction and AI to query data, identify trends and test assumptions. This allows financial planners and analysts to use trending to detect patterns in data. That way, FP&A teams spend less time searching for errors or locating trends and more time making intelligent recommendations and decisions.

2. Forecast accuracy

Forecasting is a high priority within FP&A, and AI makes it possible with predictive analysis. It can find widespread applications from customer behaviours to forecasting project spending. For instance, AI can be used to make revenue projections by accurately predicting future revenue - which can be extremely useful for investors.

3. Data modelling

Prior to today’s AI capabilities, data needed to be stored in some form of model that involved a series of relational database tables or multidimensional databases or both. Nowadays, we can have tables with defined fields and records that don’t require data to be kept in a predefined model. AI is promising to speed up analysis even more in the coming year as advancements in AI introduce sophisticated Machine Learning algorithms like decision trees and neural networks to help FP&A teams solve any issues they face.

4. Automating

AI is helping FP&A teams to create more efficient workflows for their organisation. AI makes way for robotic process automation (RPA bots) to automatically perform recurring, tedious tasks without manual intervention. In fact, a single bot can displace as much as 30 times the work of a human; reducing labour and resources that can prove costly to organisations. To paint a clearer picture, the use of RPA costs one-third of the cost for offshore employees and one-fifth for onshore employees.

5. Reporting

Data provides useful insights for FP&A teams, but too much of it can be overwhelming. When sifting through big data it can paralyse decision-making and invite the risk of error. Fortunately, with new-age AI applications, wading through big data is easy. Things like natural language generation (NLG) can help businesses cut through all the noise and find what they’re looking for. Utilising NLG simplifies risk and compliance reports by providing accurate narratives at scale; saving both time and money.

6. Personalisation

AI is helping financial planners to deliver more personalised services to their clients. One example of AI helping to personalise customer service in the finance industry is Chatbots. These apps simulate conversations with human operators. You may have interacted with one already when on a website’s landing page. Here, clients can ask questions or find information without having to speak to an actual person. It’s common in the finance and banking industry as they make financial services readily available for existing and potential clients/customers. For example, Chatbots can advise customers on things like stock prices and news based on a customer’s goals, performance and balance as well as notify clients about relevant investment opportunities based on their portfolio goals.

Conclusion

AI is helping professionals within the Financial Planning & Analysis field manage data more accurately and effectively. Today’s AI apps can offer personalisation, market analysis, forecasting and automation of routine operations across millions of data points – minimising mistakes and picking up on inconsistencies to help give organisations a competitive advantage as well as lowering the cost of resources.